Cloud-resolving simulations of deep convection over a heated mountain

Cloud-resolving numerical simulations of airflow over a diurnally heated mountain ridge are conducted to explore the mechanisms and sensitivities of convective initiation under high pressure conditions. The simulations are based on a well-observed convection event from the Convective and Orographically Induced Precipitation Study (COPS) during summer 2007, where an isolated afternoon thunderstorm developed over the Black Forest mountains of central Europe, but they are idealized to facilitate understanding and reduce computational expense. In the conditionally unstable but strongly inhibited flow under consideration, sharp horizontal convergence over the mountain acts to locally weaken the inhibition and moisten the dry midtroposphere through shallow cumulus detrainment. The onset of deep convection occurs not through the deep ascent of a single updraft but rather through a rapid succession of thermals that are vented through the mountain convergence zone into the deepening cloud mass. Emerging thermals rise through the saturated wakes of their predecessors, which diminishes the suppressive effects of entrainment and allows for rapid glaciation above the freezing level as supercooled cloud drops rime onto preexisting ice particles. These effects strongly enhance the midlevel cloud buoyancy and enable rapid ascent to the tropopause. The existence and vigor of the convection is highly sensitive to small changes in background wind speed U0, which controls the strength of the mountain convergence and the ability of midlevel moisture to accumulate above the mountain. Whereas vigorous deep convection develops for U0 = 0 m s−1, deep convection is completely eliminated for U0 = 3 m s−1. Although deep convection is able to develop under intermediate winds (U0 = 1.5 m s−1), its formation is highly sensitive to small-amplitude perturbations in the initial flow.

Impact of the quantity and flavonoid content of fruits and vegetables on markers of intake in adults with an increased risk of cardiovascular disease: the FLAVURS Trial

Purpose Limited robust randomised controlled trials investigating fruit and vegetable (F&V) intake in people at risk of cardiovascular disease (CVD) exist. We aimed to design and validate a dietary strategy of increasing flavonoid-rich versus flavonoid-poor F&V consumption on nutrient biomarker profile. Methods A parallel, randomised, controlled, dose–response dietary intervention study. Participants with a CVD relative risk of 1.5 assessed by risk scores were randomly assigned to one of the 3 groups: habitual (control, CT), high-flavonoid (HF) or low-flavonoid (LF) diets. While the CT group (n = 57) consumed their habitual diet throughout, the HF (n = 58) and LF (n = 59) groups sequentially increased their daily F&V intake by an additional 2, 4 and 6 portions for 6-week periods during the 18-week study. Results Compliance to target numbers and types of F&V was broadly met and verified by dietary records, and plasma and urinary biomarkers. Mean (±SEM) number of F&V portions/day consumed by the HF and LF groups at baseline (3.8 ± 0.3 and 3.4 ± 0.3), 6 weeks (6.3 ± 0.4 and 5.8 ± 0.3), 12 weeks (7.0 ± 0.3 and 6.8 ± 0.3) and 18 weeks (7.6 ± 0.4 and 8.1 ± 0.4), respectively, was similar at baseline yet higher than the CT group (3.9 ± 0.3, 4.3 ± 0.3, 4.6 ± 0.4, 4.5 ± 0.3) (P = 0.015). There was a dose-dependent increase in dietary and urinary flavonoids in the HF group, with no change in other groups (P = 0.0001). Significantly higher dietary intakes of folate (P = 0.035), non-starch polysaccharides (P = 0.001), vitamin C (P = 0.0001) and carotenoids (P = 0.0001) were observed in both intervention groups compared with CT, which were broadly supported by nutrient biomarker analysis. Conclusions The success of improving nutrient profile by active encouragement of F&V intake in an intervention study implies the need for a more hands-on public health approach.

A unified approach to the recognition of complex actions from sequences of zone-crossings

We present a method for the recognition of complex actions. Our method combines automatic learning of simple actions and manual definition of complex actions in a single grammar. Contrary to the general trend in complex action recognition that consists in dividing recognition into two stages, our method performs recognition of simple and complex actions in a unified way. This is performed by encoding simple action HMMs within the stochastic grammar that models complex actions. This unified approach enables a more effective influence of the higher activity layers into the recognition of simple actions which leads to a substantial improvement in the classification of complex actions. We consider the recognition of complex actions based on person transits between areas in the scene. As input, our method receives crossings of tracks along a set of zones which are derived using unsupervised learning of the movement patterns of the objects in the scene. We evaluate our method on a large dataset showing normal, suspicious and threat behaviour on a parking lot. Experiments show an improvement of ~ 30% in the recognition of both high-level scenarios and their composing simple actions with respect to a two-stage approach. Experiments with synthetic noise simulating the most common tracking failures show that our method only experiences a limited decrease in performance when moderate amounts of noise are added.